Data management and Data Visualization
Dr. Pacifique
2026-10-05
Data Visualization
Test to treat data
data1<- read.table(text="Sept_2022 Oct_2022 Nov_2022 Dec_2022 Jan_2023 Feb_2023 Mar_2023 Apr_2023 May_2023 June_2023
1 4 6 30 140 67 47 23 39 134 80
2 10 2 29 164 72 81 28 63 211 132",header=TRUE)
barplot(as.matrix(data1),main="Confirmed cases Vs Gender",beside=TRUE,col=c("gold3","red"))
legend("topright", c("Male","Female"),cex = 0.5,bty="n",fill = c("gold3","red"))
data3<- read.table(text="sept_2022 Oct_2022 Nov_2022 Dec_2022 Jan_2023 Feb_2023 Mar_2023 Apr_2023 May_2023 June_2023
1 867 582 1802 12003 3059 9205 5346 7269 5933 3245
2 842 436 1805 11785 3508 10908 4902 7883 5901 4646 ",header=TRUE)
barplot(as.matrix(data3),main=" COVID 19 test Vs Gender",beside=TRUE,col=c("blue","purple"))
legend("topright", c("Male","Female"),cex = 1,bty="n",fill = c("blue","purple"))
data2<- read.table(text="Sept_2022 Oct_2022 Nov_2022 Dec_2022 Jan_2023 Feb_2023 Mar_2023 Apr_2023 May_2023 June_2023
1 0 0 1 9 8 7 1 4 11 22
2 11 3 41 242 104 78 44 86 244 147
3 3 5 17 53 27 43 6 12 90 43 ",header=TRUE)
barplot(as.matrix(data2),main="Confirmed cases Vs Age categories",beside=TRUE,col=c("green","blue","purple"))
barplot(as.matrix(data2),main="Confirmed cases Vs Age categories",beside=TRUE,col=c("green","blue","purple"))
legend("topleft", c("Less than 15 years","15-49 years","Above 50"),cex = 1.3,bty="n",fill = c("green","blue","purple"))
months<-c("Sept_2022","Oct_2022","Nov_2022","Dec_2022", "Jan_2023","Feb_2023","Mar_2023","Apr_2023","May_2023","June_2023")
Confirmed_covid<-c(14,8,59,304,139,128,51,102,345,212)
plot(Confirmed_covid, type = "l",pch=21, col = "red",ylim=c(0,350),
xaxt="n", ylab = "Number of patients ",
main = "Confirmed cases Vs treatment ")
treatment<- c(12,0,26,217,104,57,12,47,23,20)
lines(treatment, col="blue",lty=2)
legend("topleft", legend=c("Received treatment", "Confirmed"),cex=0.5, fill = c("blue","red"))
axis(1,at=1:10,lab=c("Sept_2022","Oct_2022","Nov_2022","Dec_2022", "Jan_2023","Feb_2023","Mar_2023","Apr_2023","May_2023","June_2023"))
months<-c("sept_2022","Oct_2022","Nov_2022","Dec_2022", "Jan_2023","feb_2023","Mar_2023","Apr_2023","May_2023", "June_2023")
number_of_test<-c(1709, 1018, 3607,23788,6567,20113,10248,15152,11834,7891)
plot(number_of_test, type = "o", col = "blue",ylim=c(0,24000),
xaxt="n", ylab = "Number of Test",
main = "COVID 19 Test")
axis(1,at=1:10,lab=c("Sept_2022","Oct_2022","Nov_2022","Dec_2022", "Jan_2023","Feb_2023","Mar_2023","Apr_2023","May_2023","June_2023"))
ggplot2
ggplot2 provides a set of tools that allows you to visualize complex data sets in a new creative way -some work need some packages to get done. -some of the graph are created using R’s base graphics system
Library ggplot2
###install.packages("ggplot2")
library(ggplot2)let us explore the data mcars
data(mtcars)
#dotchart(mtcars$mpg,labels=row.names(mtcars),ce#x = 0.,main="miles per Gallon of car #model",xlab = "MPG")
table(mtcars$cyl)##
## 4 6 8
## 11 7 14#barplot(mtcars$cyl)
barplot(table(mtcars$cyl),main="Distribution of car Cylinder counts",xlab="Number of cylinders",ylab = "Number of cars")
barplot(table(mtcars$cyl),main="Distribution of car Cylinder counts",xlab="Number of cylinders",ylab = "Number of cars",horiz=TRUE)
plot(mtcars$mpg~mtcars$wt,main="Automobile Data",xlab="Weight",ylab=" Miles per Gallon")
plot(mtcars$mpg~mtcars$wt,main="Automobile Data",xlab="Weight",ylab=" Miles per Gallon")
plot(mtcars$mpg~mtcars$wt,pch=17,main="Automobile Data",xlab="Weight",ylab=" Miles per Gallon")
plot(mtcars$mpg~mtcars$wt,pch=21,main="Automobile Data",xlab="Weight",ylab=" Miles per Gallon")
plot(mtcars$mpg~mtcars$wt,type="b", lty=2, lwd=2,col="red",main="Automobile Data",xlab="Weight",ylab=" Miles per Gallon")
in ggplot2, plots are created by chaining together function using (+)sign. Each function modify the
##plot created up to that point
library(ggplot2)
ggplot(data=mtcars, aes(x=wt,y=mpg))+geom_point()+
labs(title = "Automobile Data",x="Weight",y="Miles per Galon")
ggplot(data=mtcars, aes(x=wt,y=mpg))+geom_point(pch=21,color="blue",size=1)+
geom_smooth(method="lm",color="green")+
labs(title = "Automobile Data",x="Weight",y="Miles per Galon")## `geom_smooth()` using formula = 'y ~ x'
f0<-ggplot(data=mtcars, aes(x=wt,y=mpg))+geom_point(shape=24, fill=1,size=1)+
geom_smooth(method="lm",color="blue")
f0## `geom_smooth()` using formula = 'y ~ x'
f1<-f0+theme_bw()
f1## `geom_smooth()` using formula = 'y ~ x'
f2<-f1+
theme(panel.grid = element_line(linetype = "dotted",color = "Black"))
f2## `geom_smooth()` using formula = 'y ~ x'
Grouping displays two or more groups of observations in a single plot
data(mtcars)
mtcars$am<-factor(mtcars$am, levels =c(0,1),
labels= c("Automatic","Manual"))
mtcars$vs<-factor(mtcars$vs, levels =c(0,1),
labels=c("V-Engine","Straight Engine"))
mtcars$cyl<-factor(mtcars$cyl)
ggplot(data=mtcars,aes(x=hp,y=mpg,shape=cyl,color=cyl))+
geom_point(size=3)+
facet_grid(vs~am)+
labs(title = "Automobile Data by Engine Type",x="HorsePower",y="Miles per Gallon")
data(singer,package="lattice")
ggplot(singer,aes(x=voice.part,y=height))+geom_boxplot()
### How work
Use the following function:
geom_bar() geom_boxplot() geom_density() geom_histogram() geom_hline() geom_jitter() geom_line() #geom_point() geom_rug() #geom_smooth() geom_text() geom_violin() geom_vline()
Data Management
Basic data management
cbind and rbind
While combining column wise, the number of rows must match but row names are ignored. when combining row-wise, both the number and the names of columns must match.
data2<- data.frame(x=1:3, y=c("a","b","c"))
str(data2)## 'data.frame': 3 obs. of 2 variables:
## $ x: int 1 2 3
## $ y: chr "a" "b" "c"(cbind(data2,data.frame(z=3:1)))## x y z
## 1 1 a 3
## 2 2 b 2
## 3 3 c 1(rbind(data2,data.frame(x=10,y="z")))## x y
## 1 1 a
## 2 2 b
## 3 3 c
## 4 10 zCreate another Variable
data_class<-read.table("C:\\Users\\Pacy\\OneDrive\\Desktop\\Big data course\\class_data.txt")
variable.names(data_class)## [1] "HEIGHT" "WEIGHT"head(data_class,n=5)## HEIGHT WEIGHT
## 1 161 50
## 2 155 49
## 3 158 42
## 4 170 65
## 5 160 60tail(data_class)## HEIGHT WEIGHT
## 38 164 47
## 39 163 52
## 40 168 55
## 41 157 48
## 42 164 58
## 43 154 55data_class[,1]## [1] 161 155 158 170 160 156 162 158 158 167 160 155 154 155 157 157 160 158 160
## [20] 160 152 154 150 161 162 164 161 155 159 163 159 160 158 165 156 163 155 164
## [39] 163 168 157 164 154summary(data_class$WEIGHT)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 42.00 48.00 52.00 52.47 56.00 65.00length(data_class$WEIGHT)## [1] 43data_class[,-1]## [1] 50 49 42 65 60 52 58 46 45 51 60 42 53 48 48 48 53 52 51 53 44 56 63 52 57
## [26] 49 52 54 46 50 61 55 45 63 60 56 52 47 52 55 48 58 55attach(data_class)
(BMI<-WEIGHT/(HEIGHT/100)^2)## [1] 19.28938 20.39542 16.82423 22.49135 23.43750 21.36752 22.10029 18.42653
## [9] 18.02596 18.28678 23.43750 17.48179 22.34778 19.97919 19.47341 19.47341
## [17] 20.70312 20.83000 19.92187 20.70312 19.04432 23.61275 28.00000 20.06095
## [25] 21.71925 18.21832 20.06095 22.47659 18.19548 18.81892 24.12879 21.48437
## [33] 18.02596 23.14050 24.65483 21.07720 21.64412 17.47472 19.57168 19.48696
## [41] 19.47341 21.56454 23.19109(BMI<-round(WEIGHT/(HEIGHT/100)^2,digit=1))## [1] 19.3 20.4 16.8 22.5 23.4 21.4 22.1 18.4 18.0 18.3 23.4 17.5 22.3 20.0 19.5
## [16] 19.5 20.7 20.8 19.9 20.7 19.0 23.6 28.0 20.1 21.7 18.2 20.1 22.5 18.2 18.8
## [31] 24.1 21.5 18.0 23.1 24.7 21.1 21.6 17.5 19.6 19.5 19.5 21.6 23.2head(cbind(data_class,BMI))## HEIGHT WEIGHT BMI
## 1 161 50 19.3
## 2 155 49 20.4
## 3 158 42 16.8
## 4 170 65 22.5
## 5 160 60 23.4
## 6 156 52 21.4tail(cbind(data_class,BMI),n=10)## HEIGHT WEIGHT BMI
## 34 165 63 23.1
## 35 156 60 24.7
## 36 163 56 21.1
## 37 155 52 21.6
## 38 164 47 17.5
## 39 163 52 19.6
## 40 168 55 19.5
## 41 157 48 19.5
## 42 164 58 21.6
## 43 154 55 23.2detach(data_class)Summary of BMI
summary(BMI)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 16.80 19.15 20.40 20.70 22.20 28.00hist(data_class$HEIGHT)
hist(data_class$WEIGHT,col = "blue",main = "Sample Histogram")
stem(BMI,scale=2)##
## The decimal point is at the |
##
## 16 | 8
## 17 | 55
## 18 | 0022348
## 19 | 03555569
## 20 | 0114778
## 21 | 145667
## 22 | 1355
## 23 | 12446
## 24 | 17
## 25 |
## 26 |
## 27 |
## 28 | 0Subsetting
Use of $, []. or [[]]
Creation of leadership data Interest: How men and women differ in the way they lead their organizations. 5 questions were asked in this study. Example of the question: Do men and women in management position differ in the degree to which they defer to superiors? 1: strongly disagree, 2: disagree, 3:neither agree nor disagree,4: agree, 5: strongly agree
manager <- c(1,2,3,4,5)
country<- c("US","US","UK","UK","UK")
gender<-c("M","F","F","M","F")
age<-c(32,45,25,39,99)
q1<-c(5,3,3,3,2)
q2<-c(4,5,5,3,2)
q3<-c(5,2,5,4,1)
q4<-c(5,5,5,NA,2)
q5<-c(5,5,2,NA,1)
leadership<-data.frame(manager,country, gender, age, q1,q2,q3,q4,q5, stringsAsFactors = TRUE)
leadership<-data.frame(manager,country, gender, age, q1,q2,q3,q4,q5)
str(leadership)## 'data.frame': 5 obs. of 9 variables:
## $ manager: num 1 2 3 4 5
## $ country: chr "US" "US" "UK" "UK" ...
## $ gender : chr "M" "F" "F" "M" ...
## $ age : num 32 45 25 39 99
## $ q1 : num 5 3 3 3 2
## $ q2 : num 4 5 5 3 2
## $ q3 : num 5 2 5 4 1
## $ q4 : num 5 5 5 NA 2
## $ q5 : num 5 5 2 NA 1names(leadership)## [1] "manager" "country" "gender" "age" "q1" "q2" "q3"
## [8] "q4" "q5"What you can do - combine the score of the five questions -handle the missing values - create a dataset of what you want -create age group or age categories - 99 indicate the value is missing
Missing value
leadership$age[leadership$age==99]<-NAleadership$agecat[leadership$age >75]<-"Elder"
leadership$agecat[leadership$age>=35 & leadership$age<=75]<-"Middle Aged"
leadership$agecat[leadership$age <35]<- "Young"One way to handle missing value
Deleting all observations with missing data ( Listwise deletion) is one of the several methods of handling incomplete datasets. Note: You can also replacing the missing value by the average of the remaining data.
is.na(leadership[,5:9])## q1 q2 q3 q4 q5
## [1,] FALSE FALSE FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE TRUE TRUE
## [5,] FALSE FALSE FALSE FALSE FALSEnewdata<-na.omit(leadership)
newdata## manager country gender age q1 q2 q3 q4 q5 agecat
## 1 1 US M 32 5 4 5 5 5 Young
## 2 2 US F 45 3 5 2 5 5 Middle Aged
## 3 3 UK F 25 3 5 5 5 2 YoungSorting data
order(leadership$age)## [1] 3 1 4 2 5newdata2<-leadership[order(leadership$age),]Merging data sets
To merge two data frame horizontally, you use merge() function. In most cases, two data frames are joined by one or more common key variables. -example 1: merge(dataframeA, dataframeB,by=“ID”) -example 2: merge(dataframeA,dataframeB, by=c(“ID”,“country”))
the second merge the two dataframes by ID and country
To join two data frame( datasets) vertically, use rbind() function: Note that the two data set must have the same variables
Subset (selecting variables,dropping variables,selecting observation )
newdata3<-leadership[,c(5:9)]
myvars<-c("q1","q2","q3","q4","q5")
(newdata3<-leadership[myvars])## q1 q2 q3 q4 q5
## 1 5 4 5 5 5
## 2 3 5 2 5 5
## 3 3 5 5 5 2
## 4 3 3 4 NA NA
## 5 2 2 1 2 1newdata4<-leadership[,c(-1,-2)]
newdata4<-leadership[,-(1:5)]
newdata5<-leadership[,c(-1,-7)]
leadership[[4]]## [1] 32 45 25 39 NAleadership$age## [1] 32 45 25 39 NAnewdata6<-leadership[c(-1,-3),]
newdata6<-leadership[c(2,4,5),]
newdata7<-subset(leadership,age>=35|age<24,select=c(q1,q2,q3,q4,q5))attach(leadership)## The following objects are masked _by_ .GlobalEnv:
##
## age, country, gender, manager, q1, q2, q3, q4, q5#newdata7<-leadership[gender="M" & age>30,]
(newdata7<-leadership[gender=="M" & age>30,])## manager country gender age q1 q2 q3 q4 q5 agecat
## 1 1 US M 32 5 4 5 5 5 Young
## 4 4 UK M 39 3 3 4 NA NA Middle Ageddetach(leadership)Data management with dyplr
Data source from the package nycflights13 and ggplot2 This data contains all 336,776 flights that departed from New York city in 2013.
Data management with dyplr
Data source from the package nycflights13 and ggplot2 This data contains all 336,776 flights that departed from New york city in 2013.
#install.packages("nycflights13")
#install.packages("tidyverse")
library(nycflights13)## Warning: package 'nycflights13' was built under R version 4.2.3#install.packages("dplyr")
library(dplyr)## Warning: package 'dplyr' was built under R version 4.2.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#library(tidyverse)The function filter()
#rm(list = ls())
str(flights)## tibble [336,776 × 19] (S3: tbl_df/tbl/data.frame)
## $ year : int [1:336776] 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 ...
## $ month : int [1:336776] 1 1 1 1 1 1 1 1 1 1 ...
## $ day : int [1:336776] 1 1 1 1 1 1 1 1 1 1 ...
## $ dep_time : int [1:336776] 517 533 542 544 554 554 555 557 557 558 ...
## $ sched_dep_time: int [1:336776] 515 529 540 545 600 558 600 600 600 600 ...
## $ dep_delay : num [1:336776] 2 4 2 -1 -6 -4 -5 -3 -3 -2 ...
## $ arr_time : int [1:336776] 830 850 923 1004 812 740 913 709 838 753 ...
## $ sched_arr_time: int [1:336776] 819 830 850 1022 837 728 854 723 846 745 ...
## $ arr_delay : num [1:336776] 11 20 33 -18 -25 12 19 -14 -8 8 ...
## $ carrier : chr [1:336776] "UA" "UA" "AA" "B6" ...
## $ flight : int [1:336776] 1545 1714 1141 725 461 1696 507 5708 79 301 ...
## $ tailnum : chr [1:336776] "N14228" "N24211" "N619AA" "N804JB" ...
## $ origin : chr [1:336776] "EWR" "LGA" "JFK" "JFK" ...
## $ dest : chr [1:336776] "IAH" "IAH" "MIA" "BQN" ...
## $ air_time : num [1:336776] 227 227 160 183 116 150 158 53 140 138 ...
## $ distance : num [1:336776] 1400 1416 1089 1576 762 ...
## $ hour : num [1:336776] 5 5 5 5 6 5 6 6 6 6 ...
## $ minute : num [1:336776] 15 29 40 45 0 58 0 0 0 0 ...
## $ time_hour : POSIXct[1:336776], format: "2013-01-01 05:00:00" "2013-01-01 05:00:00" ...#attach(flights)
jan<-filter(flights,month==1)
jan## # A tibble: 27,004 × 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## 7 2013 1 1 555 600 -5 913 854
## 8 2013 1 1 557 600 -3 709 723
## 9 2013 1 1 557 600 -3 838 846
## 10 2013 1 1 558 600 -2 753 745
## # ℹ 26,994 more rows
## # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
## # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
## # hour <dbl>, minute <dbl>, time_hour <dttm>newyear<-filter(flights,month==1, day==1)
newyear## # A tibble: 842 × 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## 7 2013 1 1 555 600 -5 913 854
## 8 2013 1 1 557 600 -3 709 723
## 9 2013 1 1 557 600 -3 838 846
## 10 2013 1 1 558 600 -2 753 745
## # ℹ 832 more rows
## # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
## # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
## # hour <dbl>, minute <dbl>, time_hour <dttm>dec25<-filter(flights,month==12, day==25)
dec25## # A tibble: 719 × 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 12 25 456 500 -4 649 651
## 2 2013 12 25 524 515 9 805 814
## 3 2013 12 25 542 540 2 832 850
## 4 2013 12 25 546 550 -4 1022 1027
## 5 2013 12 25 556 600 -4 730 745
## 6 2013 12 25 557 600 -3 743 752
## 7 2013 12 25 557 600 -3 818 831
## 8 2013 12 25 559 600 -1 855 856
## 9 2013 12 25 559 600 -1 849 855
## 10 2013 12 25 600 600 0 850 846
## # ℹ 709 more rows
## # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
## # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
## # hour <dbl>, minute <dbl>, time_hour <dttm>jan_dec<-filter(flights,month==1|month==12)
jan_dec## # A tibble: 55,139 × 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## 7 2013 1 1 555 600 -5 913 854
## 8 2013 1 1 557 600 -3 709 723
## 9 2013 1 1 557 600 -3 838 846
## 10 2013 1 1 558 600 -2 753 745
## # ℹ 55,129 more rows
## # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
## # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
## # hour <dbl>, minute <dbl>, time_hour <dttm>nov_dec<-filter(flights,month %in% c(11,12))
#detach(flights)The function arrange()
This change the order
data_10<-arrange(flights,year,month,day)
arrange(flights,desc(arr_delay))## # A tibble: 336,776 × 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 9 641 900 1301 1242 1530
## 2 2013 6 15 1432 1935 1137 1607 2120
## 3 2013 1 10 1121 1635 1126 1239 1810
## 4 2013 9 20 1139 1845 1014 1457 2210
## 5 2013 7 22 845 1600 1005 1044 1815
## 6 2013 4 10 1100 1900 960 1342 2211
## 7 2013 3 17 2321 810 911 135 1020
## 8 2013 7 22 2257 759 898 121 1026
## 9 2013 12 5 756 1700 896 1058 2020
## 10 2013 5 3 1133 2055 878 1250 2215
## # ℹ 336,766 more rows
## # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
## # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
## # hour <dbl>, minute <dbl>, time_hour <dttm>The function select
This helps to selects only the variables you are interested in
time_var<-select(flights, year,month, day)
select(flights, year:day)## # A tibble: 336,776 × 3
## year month day
## <int> <int> <int>
## 1 2013 1 1
## 2 2013 1 1
## 3 2013 1 1
## 4 2013 1 1
## 5 2013 1 1
## 6 2013 1 1
## 7 2013 1 1
## 8 2013 1 1
## 9 2013 1 1
## 10 2013 1 1
## # ℹ 336,766 more rowsselect(flights,-(year:day))## # A tibble: 336,776 × 16
## dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay carrier
## <int> <int> <dbl> <int> <int> <dbl> <chr>
## 1 517 515 2 830 819 11 UA
## 2 533 529 4 850 830 20 UA
## 3 542 540 2 923 850 33 AA
## 4 544 545 -1 1004 1022 -18 B6
## 5 554 600 -6 812 837 -25 DL
## 6 554 558 -4 740 728 12 UA
## 7 555 600 -5 913 854 19 B6
## 8 557 600 -3 709 723 -14 EV
## 9 557 600 -3 838 846 -8 B6
## 10 558 600 -2 753 745 8 AA
## # ℹ 336,766 more rows
## # ℹ 9 more variables: flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>The function mutate
This is useful in adding a new Variable
## create a small data set
flights2<-select(flights,year:day,ends_with("delay"),distance,air_time)
(mutate(flights2,gain=arr_delay-dep_delay,
speed=distance/air_time*60))## # A tibble: 336,776 × 9
## year month day dep_delay arr_delay distance air_time gain speed
## <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2013 1 1 2 11 1400 227 9 370.
## 2 2013 1 1 4 20 1416 227 16 374.
## 3 2013 1 1 2 33 1089 160 31 408.
## 4 2013 1 1 -1 -18 1576 183 -17 517.
## 5 2013 1 1 -6 -25 762 116 -19 394.
## 6 2013 1 1 -4 12 719 150 16 288.
## 7 2013 1 1 -5 19 1065 158 24 404.
## 8 2013 1 1 -3 -14 229 53 -11 259.
## 9 2013 1 1 -3 -8 944 140 -5 405.
## 10 2013 1 1 -2 8 733 138 10 319.
## # ℹ 336,766 more rows